An Introduction to Recognizing Functional Domains

Stormo, Gary D.

doi:10.1002/0471250953.bi0201s34

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…A MACS analysis was performed to determine peaks of enrichment of H3K27ac throughout the genome (38). The MACS analysis was conducted as recommended by comparing the ChIP DNA reads to the Input reads using a tutorial (http://liulab.dfci.harvard.edu/MACS/) (38, 39). Sequences and processed datasets have been submitted to Gene Expression Omnibus under accession number GSE75286.…”

Section: Methodsmentioning

confidence: 99%

Transcription Factor KLF5 Binds a Cyclin E1 Polymorphic Intronic Enhancer to Confer Increased Bladder Cancer Risk

Pattison

Posternak

Cole

2016

Molecular Cancer Research

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It is well established that environmental toxins, such as exposure to arsenic, are risk factors in the development of urinary bladder cancer, yet recent genome-wide association studies (GWAS) provide compelling evidence that there is a strong genetic component associated with disease predisposition. A single nucleotide polymorphism (SNP), rs8102137, was identified on chromosome 19q12, residing 6 kb upstream of the important cell cycle regulator and proto-oncogene, Cyclin E1 (CCNE1). However, the functional role of this variant in bladder cancer predisposition has been unclear since it lies within a non-coding region of the genome. Here, it is demonstrated that bladder cancer cells heterozygous for this SNP exhibit biased allelic expression of CCNE1 with 1.5-fold more transcription occurring from the risk allele. Furthermore, using chromatin immunoprecipitation assays, a novel enhancer element was identified within the first intron of CCNE1 that binds Kruppel-like Factor 5 (KLF5), a known transcriptional activator in bladder cancer. Moreover, the data reveal that the presence of rs200996365, a SNP in high linkage disequilibrium with rs8102137 residing in the center of a KLF5 motif, alters KLF5 binding to this genomic region. Through luciferase assays and CRISPR-Cas9 genome editing, a novel polymorphic intronic regulatory element controlling CCNE1 transcription is characterized. These studies uncover how a cancer-associated polymorphism mechanistically contributes to an increased predisposition for bladder cancer development. Implications A polymorphic KLF5 binding site near the CCNE1 gene explains genetic risk identified through genome wide association studies.

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Section: Methodsmentioning

confidence: 99%

Transcription Factor KLF5 Binds a Cyclin E1 Polymorphic Intronic Enhancer to Confer Increased Bladder Cancer Risk

Pattison

Posternak

Cole

2016

Molecular Cancer Research

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“…Identifying protein domains with the CDD database is a key part of understanding the function, structure, and evolution of a protein. For an introduction to protein domain analyses, we recommend UNIT 2.1 (Stormo, 2011).…”

Section: Results For Basic Protocolmentioning

confidence: 99%

Finding Homologs in Amino Acid Sequences Using Network BLAST Searches

Ladunga

2017

CP in Bioinformatics

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BLAST, the Basic Local Alignment Search Tool, is used more frequently than any other biosequence database search program. We show how to run searches on the Web, and demonstrate how to increase performance by fine-tuning arguments for a specific research project. We offer guidance for interpreting results, statistical significance and biological relevance issues, and suggest complementary analyses. This unit covers both protein-to-protein (blastp) searches and translated searches (blastx, tblastn, tfastx). blastx conceptually translates the query sequence and tblastn translates all nucleotide sequences in a database, while tblastx translates both the query and the database sequences into amino acid sequences. © 2017 by John Wiley & Sons, Inc.

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“…Finally, the unit suggests when and how domaindomain interactions may be predicted. This unit is intrinsically related with other Current Protocols bioinformatics protocols, such as Xu and Xu (2000), Krishnamurthy and Sjölander (2005), Petsko (2006), Eswar et al (2007), Coggill et al (2008), and Stormo (2011).…”

Section: Introductionmentioning

confidence: 99%

Protein Structural Domains: Definition and Prediction

Ezkurdia

Tress

2011

CP Protein Science

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Recognition and prediction of structural domains in proteins is an important part of structure and function prediction. This unit lists the range of tools available for domain prediction, and describes sequence and structural analysis tools that complement domain prediction methods. Also detailed are the basic domain prediction steps, along with suggested strategies for different protein sequences and potential pitfalls in domain boundary prediction. The difficult problem of domain orientation prediction is also discussed. All the resources necessary for domain boundary prediction are accessible via publicly available Web servers and databases and do not require computational expertise.

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An Introduction to Recognizing Functional Domains

Cited by 4 publications

References 4 publications

Transcription Factor KLF5 Binds a Cyclin E1 Polymorphic Intronic Enhancer to Confer Increased Bladder Cancer Risk

Transcription Factor KLF5 Binds a Cyclin E1 Polymorphic Intronic Enhancer to Confer Increased Bladder Cancer Risk

Finding Homologs in Amino Acid Sequences Using Network BLAST Searches

Protein Structural Domains: Definition and Prediction

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